Adaptive Neural-Network Control of Mobile Robot Formations Including Actuator Dynamics

Yan Dong Li; Ling Zhu; Ming Sun

doi:10.4028/www.scientific.net/AMM.303-306.1768

Paper Titles

Research on Micromotor Dynamometer in Surgical Power Plant
p.1748

Study on the Output Signal Modulation for Laser Gyro with Mechanical Dither Bias
p.1752

Multi-Channel Pipetting System for Automatic ELISA Instrument
p.1759

A Study on Drive Modeling and Parameter Estimation for Magneto-Rheological Fluid Damper
p.1763

Adaptive Neural-Network Control of Mobile Robot Formations Including Actuator Dynamics
p.1768

Knowledge Interchange in Task-Oriented Architecture: For Space Robot Application
p.1774

Thermal Error Modeling of a CNC Machine Tool
p.1782

Simulation on the Dynamic Characteristics of an Electromagnetic Relief Valve with High Pressure and Large Flow Rate
p.1786

Ferrite-Piezoelectric Layered Composite Materials for Magnetoelectric Tunable Dual-Band Bandpass Filter
p.1793

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 303-306Adaptive Neural-Network Control of Mobile Robot...

Adaptive Neural-Network Control of Mobile Robot Formations Including Actuator Dynamics

Abstract:

For the formation control problem of multiple nonholonomic mobile robots with actuator and formation dynamics, this paper propsed a new control strategy that integrated kinematic controller with input voltages controller of actuator. This control law was designed by backstepping technique based on formation control structure of leader-follower. The RBFNN was adopted to achieve on-line estimation for the dynamics nonlinear uncertain part for follower and leader robots. The adaptive robust controller was adopted to compensate modeling errors of neural network. This strategy not only solved the problem of parameters and non-parameter uncertainties of mobile robots, but also ensured the desired trajectory tracking of robot formation in the case of maintaining formation. The stability and convergence of the control system were proved by using the Lyapunov theory. The simulation results showed the effectiveness of this proposed method.

You might also be interested in these eBooks

Sensors, Measurement and Intelligent Materials

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 303-306)

Pages:

1768-1773

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.303-306.1768

Citation:

Cite this paper

Online since:

February 2013

Authors:

Yan Dong Li, Ling Zhu, Ming Sun

Keywords:

Actuator Dynamics, Adaptive Control, Backstepping, Formation Control, Leader-Follower, Multi-Nonholonomic Mobile Robots, Uncertainty

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

References

[1] K.D. Do. Formation-tracking control of unicycle-type mobile robots with limited sensing ranges. IEEE Transactions on Control Systems Technology, 2008, 16(3): 527–538.

DOI: 10.1109/tcst.2007.908214

Google Scholar

[2] T. Dierks, S. Jagannathan. Neural network control of mobile robot formations using RISE feedback. IEEE Transactions on Systems, Man, and Cybernetics—PART B: Cybernetics, 2009, 39(2): 332– 347.

DOI: 10.1109/tsmcb.2008.2005122

Google Scholar

[3] T. Das, I.N. Kar, S. Chaudhury. Simple neuron-based adaptive controller for a nonholonomic mobile robot including actuator dynamics. Neurocomputing, 2006, 29: 2140-2151.

DOI: 10.1016/j.neucom.2005.09.013

Google Scholar

[4] T. Dierks, S. Jagannathan. Control of nonholonomic mobile robot formations: backstepping kinematics into dynamics /16th IEEE International Conference on Control Applications Part of IEEE Multi-conference on Systems and Control, Singapore, 2007: 94-99.

DOI: 10.1109/cca.2007.4389212

Google Scholar

[5] R. Fierro , F. L. Lewis. Control of a nonholonomic mobile robot using neural networks. IEEE Transactions on Neural Network, 1998, 9(4): 589–600.

DOI: 10.1109/72.701173

Google Scholar